XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: IDENTIFYING BUILDING CHANGE USING HIGH RESOLUTION POINT CLOUDS - AN OBJECT-BASED APPROACH Steve du Plessis

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia IDENTIFYING BUILDING CHANGE USING HIGH RESOLUTION POINT CLOUDS - AN OBJECT-BASED APPROACH Steve du Plessis Global Product Line Executive Intergraph Corporation | ERDAS, 5051 Peachtree Corners Circle, Suite 100, Norcross, GA 30092 USA Steve.duPlessis@intergraph.com KEY WORDS: LiDAR, point cloud, change detection, stereo, image analysis, remote sensing, LAS ABSTRACT: High resolution point clouds provide excellent data sources for examining change over time in above-ground features such as buildings and trees. Of particular interest is the identification of illegal construction activity or damage incurred during earthquakes and other disasters. By using multi-date point cloud layers, these types of change can be efficiently identified and mapped. Such analysis is generally not as simple as differencing imagery from the two dates. Variations between the images can be caused by slight geometric mismatches between images from different acquisition dates, errors in the data returns, or natural differences caused by vegetation growth or wind direction. The factors can contribute to the detection of large amounts of inconsequential change throughout the area of interest, resulting in too many false positives for the analysis to be of any practical use. However, by conducting object-based analysis of the data — analysing meaningful objects rather than working point by point — software algorithms can be used to rapidly and accurately detect and map only the changes of interest to the customer. 1. MANUSCRIPT 1.1 Introduction Figure 1. (Left, Center) Hillshaded point clouds from November 2008 and September 2009, (Right) Point cloud indicating new buildings. High resolution point clouds provide excellent data sources for examining change over time in above-ground features such as buildings and trees. For example, an analyst can use multi-date point cloud layers to efficiently identify and map changes originating from illegal construction activity or damage incurred during earthquakes and other disasters. Such analysis is generally not as simple as differencing imagery from the two dates. Variations between the images can be caused by slight geometric mismatches between images from different acquisition dates, errors in the data returns, or natural differences caused by vegetation growth or wind direction. The factors can contribute to the detection of large amounts of inconsequential change throughout the area of interest, resulting in too many false positives for the analysis to be of any practical use. However, by conducting object-based analysis of the data — analysing meaningful objects rather than working point by point — software algorithms can be used to rapidly and accurately detect and map only the changes of interest to the customer. Data The following workflow uses LIDAR data from two dates (November 2008 and September 2009). The data is in the LAS file format. A very similar workflow could also be conducted using point cloud data from software such as LPS eATE, which auto- correlates stereo imagery to generate point clouds. 1.2 Problem — Mapping New Building Construction The primary objective of this workflow is to detect and map new building construction that occurred between the two dates. To illustrate that the same process could be used for disaster response mapping, we also performed a secondary analysis to detect and map buildings that were removed between the two dates. The analysis process consists of three major steps. First, we processed the two point cloud datasets using a variation of the traditional approach to change detection in which the “before” data is subtracted from the “after” data. This yields a difference file that represents the height difference between the two dates, with large positive height change representing new building construction and negative height change representing building loss. Second, we converted the “after” point cloud into objects using an image segmentation routine. We chose to segment the “after” data because we are looking for new building construction and those buildings should be in the “after” data but may not be in the “before” data. To detect building damage and destruction as part of a disaster assessment, you would perform the reverse— segmenting the “before” data in which the buildings are most clearly represented as objects.

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